{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda2\\envs\\stock\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "D:\\Anaconda2\\envs\\stock\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "D:\\Anaconda2\\envs\\stock\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "D:\\Anaconda2\\envs\\stock\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "D:\\Anaconda2\\envs\\stock\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "D:\\Anaconda2\\envs\\stock\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import os\n",
    "from utils_data.utilities import *\n",
    "from utils_models.models import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import math\n",
    "\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"  \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "###Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_len = 10 # Number of steps\n",
    "batch_size = 32   # Batch size\n",
    "learning_rate = 0.0001\n",
    "epochs = 10\n",
    "#n_classes = 3\n",
    "n_channels = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data_sets/train\n",
      "./data_sets/test\n",
      "(20886, 10, 7)\n",
      "(20886,)\n",
      "(256, 10, 7)\n",
      "(256,)\n",
      "16270\n",
      "4616\n"
     ]
    }
   ],
   "source": [
    "X_train, labels_train, _, _,_= read_stock_data_items_and_transform(data_path=\"./data_sets/\", split = \"train\", n_dim_feautures = 8, hold_days = seq_len, predict_days = 5, gain_threshold=2.0)\n",
    "X_test, labels_test, X_test_prices,_,_ = read_stock_data_items_and_transform(data_path=\"./data_sets/\", split=\"test\", n_dim_feautures = 8, hold_days = seq_len, predict_days = 5, gain_threshold=2.0)\n",
    "print(X_train.shape)\n",
    "print(labels_train.shape)\n",
    "print(X_test.shape)\n",
    "print(X_test_prices.shape)\n",
    "print(len(labels_train[labels_train==0]))\n",
    "print(len(labels_train[labels_train==1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Normalize?\n",
    "shuffle_ix = np.random.permutation(np.arange(len(X_train)))\n",
    "X_train = X_train[shuffle_ix]\n",
    "labels_train = labels_train[shuffle_ix]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train/Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18797, 10, 7)\n",
      "(2089, 10, 7)\n",
      "(18797,)\n",
      "(2089,)\n"
     ]
    }
   ],
   "source": [
    "X_tr, X_vld, lab_tr, lab_vld = train_test_split(X_train, labels_train, test_size = 0.1, random_state = 0)\n",
    "print(X_tr.shape)\n",
    "print(X_vld.shape)\n",
    "print(lab_tr.shape)\n",
    "print(lab_vld.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One-hot encoding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = 2\n",
    "y_tr = one_hot(lab_tr, n_classes)\n",
    "y_vld = one_hot(lab_vld, n_classes)\n",
    "y_test = one_hot(labels_test, n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct the graph\n",
    "Placeholders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = tf.Graph()\n",
    "\n",
    "# Construct placeholders\n",
    "with graph.as_default():\n",
    "    inputs_ = tf.placeholder(tf.float32, [None, seq_len, n_channels], name = 'inputs')\n",
    "    labels_ = tf.placeholder(tf.float32, [None, n_classes], name = 'labels')\n",
    "    keep_prob_ = tf.placeholder(tf.float32, name = 'keep')\n",
    "    learning_rate_ = tf.placeholder(tf.float32, name = 'learning_rate')\n",
    "    is_training_ = tf.placeholder(tf.bool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build Convolutional Layers\n",
    "\n",
    "Note: Should we use a different activation? Like tf.nn.tanh?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\AI\\Project\\stock\\Stock-prediction-IX\\predict\\utils_models\\models.py:15: conv1d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.keras.layers.Conv1D` instead.\n",
      "WARNING:tensorflow:From D:\\Anaconda2\\envs\\stock\\lib\\site-packages\\tensorflow_core\\python\\layers\\convolutional.py:218: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n",
      "WARNING:tensorflow:From D:\\AI\\Project\\stock\\Stock-prediction-IX\\predict\\utils_models\\models.py:23: batch_normalization (from tensorflow.python.layers.normalization) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.BatchNormalization instead.  In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.batch_normalization` documentation).\n",
      "WARNING:tensorflow:From D:\\AI\\Project\\stock\\Stock-prediction-IX\\predict\\utils_models\\models.py:27: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From D:\\AI\\Project\\stock\\Stock-prediction-IX\\predict\\utils_models\\models.py:30: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Dense instead.\n",
      "WARNING:tensorflow:From <ipython-input-8-5b377209672a>:6: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with graph.as_default():\n",
    "    logits, output = cnn_models(inputs_, is_training_, keep_prob_, n_classes_=n_classes)\n",
    "    # Cost function and optimizer\n",
    "    #loss = tf.losses.huber_loss(labels=labels_, predictions=logits, delta=1.0)\n",
    "    #cost = tf.reduce_mean(loss)\n",
    "    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels_))\n",
    "\n",
    "    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):    \n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate_).minimize(cost)\n",
    "    \n",
    "    # Accuracy\n",
    "    correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(labels_, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/10 Iteration: 100 Train loss: 1.351553 Train acc: 0.625000\n",
      "Epoch: 0/10 Iteration: 200 Train loss: 1.064886 Train acc: 0.562500\n",
      "Epoch: 0/10 Iteration: 300 Train loss: 0.760108 Train acc: 0.593750\n",
      "Epoch: 0/10 Iteration: 400 Train loss: 1.125217 Train acc: 0.468750\n",
      "Epoch: 0/10 Iteration: 500 Train loss: 1.681863 Train acc: 0.593750\n",
      "Epoch: 0/10 Iteration: 500 Validation loss: 0.585001 Validation acc: 0.779327\n",
      "Epoch: 1/10 Iteration: 600 Train loss: 0.606256 Train acc: 0.687500\n",
      "Epoch: 1/10 Iteration: 800 Train loss: 1.343374 Train acc: 0.531250\n",
      "Epoch: 1/10 Iteration: 1000 Train loss: 0.534806 Train acc: 0.718750\n",
      "Epoch: 1/10 Iteration: 1000 Validation loss: 0.542050 Validation acc: 0.777404\n",
      "Epoch: 2/10 Iteration: 1200 Train loss: 0.976916 Train acc: 0.625000\n",
      "Epoch: 2/10 Iteration: 1400 Train loss: 0.610303 Train acc: 0.656250\n",
      "Epoch: 2/10 Iteration: 1500 Validation loss: 0.545133 Validation acc: 0.779327\n",
      "Epoch: 2/10 Iteration: 1600 Train loss: 0.608167 Train acc: 0.656250\n",
      "Epoch: 3/10 Iteration: 1800 Train loss: 0.721704 Train acc: 0.687500\n",
      "Epoch: 3/10 Iteration: 2000 Train loss: 0.423892 Train acc: 0.718750\n",
      "Epoch: 3/10 Iteration: 2000 Validation loss: 0.600828 Validation acc: 0.779327\n",
      "Epoch: 3/10 Iteration: 2200 Train loss: 0.849461 Train acc: 0.656250\n",
      "Epoch: 4/10 Iteration: 2400 Train loss: 0.670924 Train acc: 0.656250\n",
      "Epoch: 4/10 Iteration: 2500 Validation loss: 0.594645 Validation acc: 0.779327\n",
      "Epoch: 4/10 Iteration: 2600 Train loss: 0.590732 Train acc: 0.718750\n",
      "Epoch: 4/10 Iteration: 2800 Train loss: 1.169391 Train acc: 0.718750\n",
      "Epoch: 5/10 Iteration: 3000 Train loss: 0.511764 Train acc: 0.843750\n",
      "Epoch: 5/10 Iteration: 3000 Validation loss: 0.626485 Validation acc: 0.779327\n",
      "Epoch: 5/10 Iteration: 3200 Train loss: 0.598786 Train acc: 0.750000\n",
      "Epoch: 5/10 Iteration: 3400 Train loss: 0.541962 Train acc: 0.718750\n",
      "Epoch: 5/10 Iteration: 3500 Validation loss: 0.627524 Validation acc: 0.779327\n",
      "WARNING:tensorflow:From D:\\Anaconda2\\envs\\stock\\lib\\site-packages\\tensorflow_core\\python\\training\\saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to delete files with this prefix.\n",
      "Epoch: 6/10 Iteration: 3600 Train loss: 1.231033 Train acc: 0.781250\n",
      "Epoch: 6/10 Iteration: 3800 Train loss: 0.667075 Train acc: 0.812500\n",
      "Epoch: 6/10 Iteration: 4000 Train loss: 0.538201 Train acc: 0.718750\n",
      "Epoch: 6/10 Iteration: 4000 Validation loss: 0.577959 Validation acc: 0.779327\n",
      "Epoch: 7/10 Iteration: 4200 Train loss: 1.273644 Train acc: 0.750000\n",
      "Epoch: 7/10 Iteration: 4400 Train loss: 0.778345 Train acc: 0.687500\n",
      "Epoch: 7/10 Iteration: 4500 Validation loss: 0.561086 Validation acc: 0.779327\n",
      "Epoch: 7/10 Iteration: 4600 Train loss: 1.099979 Train acc: 0.687500\n",
      "Epoch: 8/10 Iteration: 4800 Train loss: 0.827612 Train acc: 0.531250\n",
      "Epoch: 8/10 Iteration: 5000 Train loss: 0.690076 Train acc: 0.750000\n",
      "Epoch: 8/10 Iteration: 5000 Validation loss: 0.557204 Validation acc: 0.779327\n",
      "Epoch: 8/10 Iteration: 5200 Train loss: 0.384894 Train acc: 0.875000\n",
      "Epoch: 9/10 Iteration: 5400 Train loss: 0.381536 Train acc: 0.843750\n",
      "Epoch: 9/10 Iteration: 5500 Validation loss: 0.547464 Validation acc: 0.779327\n",
      "Epoch: 9/10 Iteration: 5600 Train loss: 0.430062 Train acc: 0.843750\n",
      "Epoch: 9/10 Iteration: 5800 Train loss: 0.497642 Train acc: 0.812500\n"
     ]
    }
   ],
   "source": [
    "validation_acc = []\n",
    "validation_loss = []\n",
    "\n",
    "train_acc = []\n",
    "train_loss = []\n",
    "\n",
    "pre_acc = 0\n",
    "\n",
    "reload = False\n",
    "#reload = True\n",
    "#tf.set_random_seed(1)\n",
    "#tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)\n",
    "\n",
    "if (os.path.exists('checkpoints-cnn-max') == False):\n",
    "    !mkdir checkpoints-cnn-max\n",
    "    !mkdir checkpoints-cnn\n",
    "\n",
    "with graph.as_default():\n",
    "    saver = tf.train.Saver(tf.global_variables(), max_to_keep = 5)\n",
    "    saver2 = tf.train.Saver(tf.global_variables(), max_to_keep = 1)\n",
    "\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    iteration = 1\n",
    "    \n",
    "    if reload == True :\n",
    "        saver.restore(sess, tf.train.latest_checkpoint(r'checkpoints-cnn'))\n",
    "        print(\"Model restored...\")   \n",
    "\n",
    "    # Loop over epochs\n",
    "    for e in range(0, epochs):\n",
    "\n",
    "        # Loop over batches\n",
    "        for x,y in get_batches(X_tr, y_tr, batch_size):\n",
    "            # Feed dictionary\n",
    "            feed = {inputs_ : x, labels_ : y, keep_prob_ : 0.15, learning_rate_ : learning_rate, is_training_:True}\n",
    "            # Loss\n",
    "            loss, _ , acc, logits_ = sess.run([cost, optimizer, accuracy, logits], feed_dict = feed)\n",
    "            #if math.isnan(loss) :\n",
    "            #    print(x)\n",
    "            train_acc.append(acc)\n",
    "            train_loss.append(loss)\n",
    "            #print(y)\n",
    "            # Print at each 5 iters\n",
    "            if (e==0 and iteration % 100 == 0):\n",
    "                print(\"Epoch: {}/{}\".format(e, epochs),\"Iteration: {:d}\".format(iteration),\"Train loss: {:6f}\".format(loss),\"Train acc: {:.6f}\".format(acc))               \n",
    "            elif (e>0 and iteration % 200 == 0):\n",
    "                print(\"Epoch: {}/{}\".format(e, epochs),\"Iteration: {:d}\".format(iteration),\"Train loss: {:6f}\".format(loss),\"Train acc: {:.6f}\".format(acc))\n",
    "            # Compute validation loss at every 10 iterations\n",
    "            if (iteration%500 == 0):                \n",
    "                val_acc_ = []\n",
    "                val_loss_ = []\n",
    "                for x_v, y_v in get_batches(X_vld, y_vld, batch_size):\n",
    "                    # Feed\n",
    "                    feed = {inputs_ : x_v, labels_ : y_v, keep_prob_ : 1.0, is_training_: False}  \n",
    "                    \n",
    "                    # Loss\n",
    "                    loss_v, acc_v = sess.run([cost, accuracy], feed_dict = feed)                    \n",
    "                    val_acc_.append(acc_v)\n",
    "                    val_loss_.append(loss_v)\n",
    "                \n",
    "                if (e == 0):\n",
    "                    print(\"Epoch: {}/{}\".format(e, epochs),\"Iteration: {:d}\".format(iteration), \"Validation loss: {:6f}\".format(np.mean(val_loss_)),\"Validation acc: {:.6f}\".format(np.mean(val_acc_)))\n",
    "                    pre_acc = np.mean(val_acc_)\n",
    "                elif (e > 0):\n",
    "                    print(\"Epoch: {}/{}\".format(e, epochs),\"Iteration: {:d}\".format(iteration), \"Validation loss: {:6f}\".format(np.mean(val_loss_)),\"Validation acc: {:.6f}\".format(np.mean(val_acc_)))\n",
    "                    if np.mean(val_acc_) > pre_acc :\n",
    "                        print(np.mean(val_acc_))\n",
    "                        print(pre_acc)\n",
    "                        pre_acc = np.mean(val_acc_)\n",
    "                        save_max_acc_path = \"checkpoints-cnn-max/stock_bank_\"+ str(e) + \"_\" + str(pre_acc) + \".ckpt\"\n",
    "                        saver2.save(sess, save_max_acc_path) \n",
    "                # Store\n",
    "                validation_acc.append(val_acc_)\n",
    "                validation_loss.append(val_loss_)\n",
    "            \n",
    "            # Iterate \n",
    "            iteration += 1\n",
    "        save_path = \"checkpoints-cnn/stock_bank_\"+ str(e) +\".ckpt\"\n",
    "        saver.save(sess, save_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1872x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1872x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and test loss\n",
    "t = np.arange(len(train_loss))\n",
    "q = np.arange(len(validation_loss))\n",
    "plt.figure(figsize = (26,6))\n",
    "plt.plot(t, np.array(train_loss), 'r*')\n",
    "plt.legend(['train'], loc='upper right')\n",
    "plt.figure(figsize = (26,6))\n",
    "plt.plot(q, np.array(validation_loss), 'b*')\n",
    "plt.xlabel(\"iteration\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(['validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1872x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1872x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Accuracies\n",
    "plt.figure(figsize = (26,6))\n",
    "plt.plot(t, np.array(train_acc), 'r*')\n",
    "plt.legend(['train'], loc='upper right')\n",
    "plt.figure(figsize = (26,6))\n",
    "plt.plot(q, np.array(validation_acc), 'b*')\n",
    "plt.xlabel(\"iteration\")\n",
    "plt.ylabel(\"Accuray\")\n",
    "plt.legend(['validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from checkpoints-cnn-max\\stock_bank_2_0.77355766.ckpt\n",
      "Test accuracy: 0.703125\n",
      "Tommorow stock : \n",
      "7.8515625\n"
     ]
    },
    {
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mId9BCE8wD0Qs/Eb3WfPAHQDQuaoq6dhjE38vKrIA/rjjkl+/sVF64w3p+eft+NWv7H1GjLBj3DjpmGOksjLp17+WfvGLxO3W1Egf/aj02GM2Osa5trc/YoSdd+6UDj88s58rAABAhFKtru13zh0f/sU5N01STc8sCchxaQbu6UhlE9dt21LY2LVWuuTjG3SRfqv+hfZS5F6dHw+gZ+3fLw0YEPUqOkbgDgDp2bVLGjo09evn5UknnmgjX154wUL0qirpnXekV1+VnnxS+s1v7E7b7t3SypXSPfdI//RP0q232gibz30uedgutQzcAQAAskiqDfdvSHrEOff3g38fK+nCnlkSkOPq6hIbkvaC9hryrS9r045/vVCjtUe1B1zvz4/vZEQNDXmgm+LQcC8qsjOBOwB0rrHRQvF0AvfW2gvOJSk/32bDT5pkY2JSQeAOAACyVEoN9yAIXpd0pKSrJF0t6aggCN7oyYUBOSkIpPr6Hmu4d0ebdvx3X9d2jdKVF1S0aMdLKTbkL2nbhJ87V5ozp3vteImGPNBtcQjcabgDQOp277ZzdwL3TBs+3M4VFdGuAwAAIMM6DNydc6cfPM+VdK6kj0maKOncg5cByKTGRgvdPQzc2ygo0CKdr9uu29BiRI2U2uiaZCH86NHJN3ZNdURNeKQazicL4QnmATFSBgCyTVWVnX0K3IcMsbE1NNwBAECW6azhfurB87lJjnN6cF1Abqqrs3McAvdw7E19fUpXz+j8+CQhPPPjgQyi4Q4A2SUM3IuLo11Hc87ZWBkCdwAAkGU6nOEeBMH3nXN9JP0pCIKHe2lNQO6KU+AerjFccxd0eX781rZNeObHAxlUXS2NHRv1KjpG4A4AqfOx4S4RuAMAgKzU6Qz3IAiaJF3TC2sBEIbXvbhpapel2XDvjlTb8RLz44GMYKQMAGQXAncAAIBek9KmqZKec859yzl3qHNuWHj06MqAXJRjDffuSBbCt3c58+OBNDFSBgCyC4E7AABAr0k1cP+KpKsl/UXSsmYHgEwK2+JxCNx7seHeXcyPB9K0fz+BOwBkE18D9+HDpYqKqFcBAACQUR3OcG/maFngPktSIOllSXf01KKAnEXDvWnoTt4AACAASURBVNfEeX68lHxWPPPjkRFBYP9hGSkDANlj1y6771ZUFPVKWhoxwgL3piZrKgAAAGSBVO/V3CfpKEm/lHTrwT/f11OLAnJWnAL3GDXcu8PH+fESDXn0oPp6qbGRhjsAZJOqKmu3Oxf1SloaMcJ+5+zeHfVKAAAAMibVhvsRQRBMafb3xc65N7v7wZ1zd0s6R1J5EASTunt7QOzFKXCPecO9O9prxye7PJWG/OjRVirurB0/Z4706KMtHyt3pSFPOx4d2r/fzgTuAJA9wsDdNyNG2HnnTj/XBwAA0AWpNtz/1zk3I/yLc+4kSa9k4OPfK2l2Bm4HyA5heB22x32WIw337srk/PjBg6Wysu435GnHo0PV1Xb2faRM3772n5rAHQA6F4fAHQAAIEuk2nA/SdKXnHObD/59gqS1zrmVkoIgCCZ35YMHQfCSc660K+8LZKU4bZqaww337urO/PixY9sG8ak25Jkfj5SEgbvvDXfnEv/hAQAdq6qSRo2KehVtEbgDAIAslGrDfbakwySdevA4TNJnZONgzu2ZpRnn3BXOuWXOuWU7duzoyQ8FRC9OI2VouPe4ZO14qesNeebHIyVxGSkjEbgDQKp8bbgPH27niopo1wEAAJBBKTXcgyDY1NML6eBjL5S0UJKmT58eRLUOoFfEKXCn4R6Z7jTkfZwfD8/EZaSMROAOAKnyNXCn4Y5ssnu3tV9OPz0eI0IBAD0m1YY7gN4Qp8Cdhrv34jI/PlkTnnZ8hOIyUkYicAeAVDQ1Sbt2+Rm4Dxpk9ykJ3BF3VVUWtM+eLR12mHTzzVJlZfduc80au6M9ZYr02c9K114r3XKL3al///3MrBsA0CNSneEOoDfEKXDPy7MUlcA9VnybH99eE755MN/Z/HhkGCNlACC77Nljv6x9DNyds5Y7gTvibPdu6ayzpFWrpB/9SHr2Wem737U7sl/6kvS1r0lHH93ypaIdKS+XfvADuxM+cKA0c6Y1X154Qdq3z65TUiJt3NhTnxEAoJsibbg75x6Q9KqkI5xzW5xzl0W5HiByYeAeh5cgOmfrZKRMVuqN+fHtNeGdS29+PA35DGOkDABkl1277Oxj4C4RuCPe9uyxVvuKFTZ/8TvfkZ5/XnrrLbvze++90qRJ0rBh0ic+IV11ld25fvFFafVqadMm28PgwAG7T/PjH0sTJ1rYfvXV0rvvSv/zP3Z7e/bYda+6yhruTU1Rf/YAgHZE2nAPguALUX58wDthWzwODXfJ1knDPadkcn58sib87NlWwnvmmdTmx6fTkKcdnyJGygBAdqmqsrPPgTubpiKO9u6V/uEfpGXLpIcfls49N/G2Y4+V7rxT+o//kB55RFq50hrwDzxgjfhknLM7wueea8H7kUe2ffuwYXZ5U5ONrAn3QQAAeIWRMoBP4jRSRqLhjnYlC+a7uolrOD/+W99KbUxNc13ZxDXng/lwpExcGu41NVGvAgD85nvgPny4BZGAr8I7pw0NdjQ22v2Piy+WXntNevBBa4gkM3KkNdWb39YHH0hr19qrT/btSxz799sc+NNP73g9I0faeccOAncA8BSBO+CTuAXuNNyRhlRDeCm1+fGpNuSZH5+muDXc9+6NehUA4LcwcC8ujnYd7WGkDHw3b5412Fvr00f63e+k889P/back8aPt6OrmgfuRx3V9dsBAPQYAnfAJ3EL3Gm4o5tSHVEjJWbFp9uQT2cT1+aaB/M1NamPqYl9MF9dbQ8GCwujXknnGCkDAJ3zveE+YoSNxmhslPLyol4N0Nby5dJxx1mjPT8/cUyZIs2Y0fvrCQP38vLe/9gAgJQQuAM+idOmqRINd/Sq7jTkmR+fhv377ZNv/kn7isAdADoXh8C9qcnGawwfHvVqgLYqK6Uzz5Suuy7qlZjmDXcAgJf6RL0AAM2E4XVcAnca7ojYokUWvk+ZYudFi5JfFrbjly618969bUfUjB7dNpgP58dfdFFiwkr//vYK4rAh39RkZ+fsaH1ZUVHLED60dat06qnStm0dX9brwmcb4oDAHQA6V1VlzfGBA6NeSXLhDGrGysBHjY32PeTTk0Hh9wyBOwB4i8Ad8EldnT0gisvLaWm4IyZSCeG3bUt+WXvz41uH8HPnWiM+lWC+vRA+2WVSLwfxYcM9DgjcAaBzVVXWbvf1lUtheFhREe06gGR277aXQA4bFvVKEgoKbE8GAncA8BYjZQCf1NXFZ367RMMdscb8+HZUV0sDBnTxnXsZgTsAdC4M3H1Fwx0+q6y0s08Nd8nGyhC4A4C3aLgDPolb4E7DHTmiqw35ZKNrUm3HX3yxBfjpjKlJdXRNhxgpAwDZZdcuvwP3MMgkcIePwlde+NRwlwjcAcBzBO6AT+IWuNNwRw7L2vnxVf3iFbg3NNgBAEiOhjvQdWHD3cfAvbw86lUAANpB4A74pK4uPhumSjTcgRTEbn78exe1GCnjxUau7SkstPOBA9GuAwB85nvgPmCA1K8fgTv8FDbcGSkDAEgDM9wBn9TXx6/hvmdP1KsAYsfr+fE7LtCCP12gwqII5senKwzca2vjM3ceAHpbVZVtsOgr56zlTuAOH/nacB81yr5nmpqsZQEA8Ao/mQGfxG2kDA13oMf1+vx4V6OLD3slmvnx6WoeuAMA2goC/xvukgXuYZMY8EkYuPv2pNXIkVJjo+3RAADwDoE74JO4Be7McAci0aPz44MCDS5q6NX58R1d3iECdwDo2L59FsrFIXCn4Q4fVVRY2J7v2XCAkSPtzFgZAPASgTvgk7gF7jTcAa91qR2ff5e21Q3t1fnxUhcb8gTuANCxqio7+x64Dx9O4A4/VVb6N05GInAHAM959jQtkOPiFrjTcAdip8P58UGg2xqvlObdIGlyr8+Pb355YaF06aWdzI8/GLhv3dKgeVf38vx4AIiDuATuNNzhq8pK/zZMlRKBe3l5tOsAACRFwx3wSV2dhdhxQcMdyC61tZaeH9yAtKfnx198sbRiReqja9o05A8G7jfdMar358cDQBzEKXCvqrJnawGfVFTQcAcApI3AHfBJfT0NdwDRqa62c5h+J5HJ+fGDB9v7pBLOJw3hzzpTToEWPD629+fHA0AchBsqxiFwDzd4BXzCSBkAQBcQuAM+ieNIGRruQPbYv9/OHQTuqUq1HS91vSE/97QKzdHv1b9fw4eX9dr8eACIgzg13CVrEwM+qajwc6RMv352Z4nAHQC8ROAO+CRugXtBAQ13IJuEDfeDI2UyLVkI397lqYTwo0dJo1Wu2rq89NvxLnGkGs4nC+EJ5gF4LW6BO3Pc4ZPGRnuViI8Nd8la7gTuAOAlAnfAJ3EL3Gm4A9klhZEyvSWlhnxVgbZrlK48/e3enx9/EO14AF6rqrIfYoMGRb2SjoUNYgJ3+CQcyeRjw10icAcAjxG4Az6J46apDQ02cxNA/GVwpExPaBPC37VLi3S+bvvCkt6fH5+BdnxHlwNARlRVScXF9oPMZzTc4aNwxJHPDffy8qhXAQBIwvN7XkCOieOmqRItdyBb9PBImYwrLLRzbW2Li3tlfvxcac6c7rXjJRryAHpYVZX/42QkAnf4qbLSzj4H7jTcAcBL+VEvAEAzcRspE641busGkJznDfc22gnckwnnxUsWwnd0+dy5Fr5fcYW0cKEF4KNHt5ofP9pe3NM6mG9okB5/3J676N/fLg9DeMnO4Z+bCy8vLJQuvTQRwt9+u71961Zp3jzpoYekMWPavwwAWohL4N6/vz07SeDesZdekn74Q3u2uLi45XHOOdKnPhX1CrNLGLj7PFJm5067Q+Jc1KsBADRDwx3wSdyCaxruQHbxaIZ7StII3NORakO+q+145scD6DVxCdwla7mHIzx8t3q19Nxz0uuvS++8Yy3jurqe+3ivvSadeab9gH/rLenQQ+0XxXvvSX/+s/2yuu66nvv4ucr3kTKjRtnjsN27o14JAKAVGu6AT+IWuDdvuAOIv7iNlMnPT6TUPay9hnzry1Jpx3c0P74rDfmutOM7uhxAFqmqsnA2DkaMiEfDvb5emjkzecg5ZIg0fnzLY+xYayKPGGHH8OH2C6C6Wtq3L3FUV9vvtPz8xLF3r3TLLdKTT9r7/vSn0lVX2TOxzV1zjXT//b3z+eeSODTcJXvCp7g42rUAAFogcAd8ErfAnYY7kF3iNlLGuURa7YlUR9RIiYZ8R+F8shB+9mx79fgzz3QezLcXwkstG/KdhfMAYmrXrvg03IcPj0fgvmyZhe3/8R/Sscfa13jXLntyo7xc2rLFjrfespcbBUH3Pl5xsfT//p/09a9LAwcmv05Jia1p924L/ZEZFRV2X8PXr2nzwH3ixGjXAgBogcAd8EUQWOAehthxQMMdyC5xGykjeRe4J8P8eACRCIL4jZTZuDHqVXTuhRfsfPnlnTef6+sthN+5s+WxZ4+9mmzgwMTRv7/9mzU0JI4gkE45pfP2ckmJnTdvticBkBmVlfa1z8uLeiXJNQ/cAQBeIXAHfNHYaGca7gCiUl1tDyrj9HOosFCqqYl6FRmTTkO+K+34OXOkb39b+vGPUwvn2wvhaccDMVBTY6WIOAXucWi4L15sc8FSGTPSt680bpwdPSkM3DdtInDPpMpKf8fJSInAvbw82nUAANpg01TAF2FLPE5BFw13ILvs32/pq3NRryR1MWi4d1eyTVxT2dg12SauHc2PT3UTV+dS39i1vU1c2dwV6AVVVXaOy2znESNsNEtDQ9QraV9trfTKK9Lpp0e9kpYmTLDzpk3RriPbVFT4u2GqRMMdADxG4A74Io6BOw13ILuEdec4yYHAPVWphPBhwJ1KOJ8shJ8711ryqQTz7YXwUnrhPIAuCgP3ODXcpcRGlT5autR+4H3yk1GvpKXRo+0xxObNUa8ku/jecC8qsnFEBO4A4B0Cd8AXcQzcabgD2aW62mbKxgmBe4eShfDtXZ5KCD96dPLRNem047vbkCeYB1IU18Dd57EyixfbD7hTTol6JS316WMtdxrumVVZ6XfDXbKWO4E7AHiHwB3wRRwDdxruQHYJR8rECYF7xqTakO9qO/7ii6UVK1IP59sL4WnHAymKW+AeNol9DtxfeEGaNk0aMiTqlbRVUkLgnmm+j5SRCNwBwFME7oAvwtA6DLHjgIY7kF0YKYNW4jw/XqIhjxwXt8Dd94b7/v3Sa6/5N04mRMM9sxoapN27/R4pIxG4A4CnCNwBX9BwBxA1Rsqgi3ycHy/RkEeOI3DPrFdesfu8vm2YGiopsR9mBw5EvZLsEH7/xKHhXl4e9SoAAK0QuAO+iGPgTsMdyC6MlEEGRTU//uKLpSBI3oZnfjxyyq5ddvZx/Ekyvo+UWbxYys+XZs6MeiXJlZTYecuWaNeRLcLNe+MQuO/YYb/4AADeyI96AchC778v3XWXNUCamjo+JGnsWLuDWFpqxyGHSHl5UX4G0Yhj4E7DHcguNNwRkTCMlyyEl6zRfuWV0hVXSAsXWtgttb2sdQg/eLBUViZ961vS448nJiXNnm15xDPPJC6rrU005CU7L1hgt3XppYkQ/vbb7e3Ng/nwsq1bpXnzpIceksaM6Z2vF5CSqioL2+Nyv7qoyH4HVVREvZLkXnhBOukkaeDAqFeS3IQJdt60STr88GjXkg3CwN33kTKjRtnjyPCZagCAFwjckXm33Sb96EfWAOnTp+OjqSlxZyaUn28vKS0ubnl85CPSjBl2R3fUqMT1g0B6803p6aftUXRZmT2qPv986bTT7PbiII6BOw13ILswwx0eSRbCN9dRMD92bPKGfBC0bcg3NLQM5tsL4ZvrLJiXkgfxhPPoVVVV8RknExoxws+G++7d0rJl0ve+F/VK2hc23JnjnhnhEz9xaLhL1nIncAcAb8QkiUSsvPWWvU58xYrUrl9bK23eLG3cmDh27rSXwe7aZXd23n1XevRRe1QsWfh+0knWsH722cRru6dOlY47Trr/fulXv7JGwpw59vry007L/OeaSWFoHadNU2m4A9mFkTKIofaC+XBMTboN+WQhfLrt+Jqa5G14GvLoVVVVVlqJk+HD/QzcX37ZikK+zm+XpEMPtTlZmzdHvZLsEJeGe/PAnVc2AIA3CNyReStXphduFxZKH/uYHR2prpaWL7cBr0uXSn/5iz3S/fSn7ZHwWWdZpS287jPPWEj/0EPSnXdKf/ubdMIJXf60elwYWtNwBxAVRsogi3SnId86hE+1HT9njt31cC7xcbrSkKcdj4yg4Z45ixdL/fpJJ58c9UraV1Bgj4VouGdGnGa4Sxa4AwC8QeCOzKqqso16jj0287fdv780a5YdqVx3zhw7/v53adw4ezTrc+Aex5EyNNxzz5Yt9uqSMHVqfowYYSlQ86OkRProR+3BQPMECv5pbLR/VxruyDHMj0fWqqqSjjwy6lWkZ8QIe2Wrb154Qfr4x+0b1GcTJhC4Z0pFhY1A9X3T4TBwLy+Pdh0AgBYiDdydc7Ml/UJSnqQ7gyC4Ocr1IANWrbJzTwTuXXXIIXYsXx71SjoWx8CdhnvuWbbMnsSaN8+a0LW10oEDliDt3CmtW2cjnlr/nxg0yIL39sL3Pn3aBvjOWVtn27bEsWOHbf7W/Hr9+0uXXWajo9B1NTV2jmPg3thoVeO47NkB7zE/HlmBhntmVFTYflH/9m9Rr6RzJSXSG29EvYrsUFlp3z99+kS9ko7RcAcAL0X2yNQ5lyfpNkmflrRF0uvOuSeCIFgT1ZqQAStX2nnSpGjX0dq0af7f+Yxj4E7DPfds2GDn//qv9mdaBoHtv7B1q+3J8O67iWPFirYbJUsWmB44YElTECQuz8+3pGrMGHvibMoUm6EaplLhHhCXXGLts1tvjV9g7IvqajvHcaSMZP8XBg6Mdi3IOcyPh9fiGrjv2WP3LX3Z1+gvf7FvQp/nt4dKSqTHHrP7Sr4Hxb6rqPB/nIxk99uKigjcAcAzUVbBTpT0bhAEGyTJOfegpM9KInCPs5Ur7WV348dHvZKWjj9e+uMfpX37/A1k4hi403DPPWVl1lbv6AGIc/YAf+hQ6eij07v9ILAH2bW1FsIPGdL5A8aGBukHP5B++EPbq+GRR+L3Enof7N9v57g9YUHgDg8xPx6RC5+ViWPgLlnY6ct/tsWL7ZvM59GUoZISu1++fXtibyt0TWWl/xumhkaNInAHAM9EGbiPk/R+s79vkXRSRGtBpqxcaeNkfJvVPG2aPVpdsSK1GfBRCENrX9o8qaDhnnvKyqSPfKTnvsedsydy0nniKT9f+vd/lz7xCUuhpk+X7rjD/tzb9uyRnnvOxt/k57c8xoyRZs70d/5r2HCPc+AOxAzz49Fjdu2yc1wD9xNPtLUPHGjHgAE2Uq61QYPsfcJj5MjUf4/V1Ni887Iye0Xexo02B7ukxJ64D49nn7X7GHEoxUyYYOdNmwjcu6uy0n7QxsHIkQTu8NMnP2kZTOt9voYMaftYacgQezXzMcfYJtXdtWePfR+Hd3rCV1NPnGivnAZ6WJSBe7K0JmhzJeeukHSFJE0I70DAT0FgM9wvuijqlbQ1bZqdly/3N3APQ+s43JkP9eljD35ouOeOsjLpYx+LehXJnXWW3aG76CLpi1+0hGjIkMSD9YED7c5b6ycLCgpsY+Xx4xPHmDHJ54GHTwY0v41337VX0Pzxj9JLL3X8BFRRkd3xnD3bjokTM/O5p2vfPmn9evtc+vWzlG3LFntbnEfKAFmA+fHIiKoqO8ctcD/jDOnaa22O+/799vtqzx7bP6apqeV1g0Dau9eCxgMHuv4xCwul0lI7jj7agvc//EG6887EdS67rOu335tKSuy8ebM0Y0a0a4m7igrpqKOiXkVqRo5k01T4p7paevFF+1l06KFWSHrjDbtTsG9f+++Xn2/fe1On2uPOAwfs+uFRUyMVF7d8snXoUOmDD2w/sfAIGwutTZtm+5IBPSzKwH2LpEOb/X28pL+3vlIQBAslLZSk6dOntwnk4ZEtW6Tdu/3aMDV0yCH2CM/nOe5xHCkjWcudhntuCAIL3M86K+qVtG/cOOnPf5ZuuUV6/fXEHbP337dzsgfkNTXpb9AWbtial2cPyCR7kP7Nb0rnnCMdcURiI8/wePttq5g+/bT01FP2PqNHW8AdBvntHf362XWbPykwbpwla+Hb8/Pbf+VBQ4N9PZ5/3o5XX23/+3bQoPS+FlEjcEeOYn48OhTXwH3oUOnnP0/vfYLAwvmdOy18T/X3Qb9+FlCPGpX892dFhT05vXmz/W6PgzBw37Qp2nVkgziNlBk50opvyIwNG2zvhiFDLNgNj4ED2/6syMuzt3V3z4Tt2604tGKF/cxpvl/VgQOWZ9xxR7z2ZnjnHTt/4xvShRe2fFsQtH2sVF5uG1SHX4c//1n6zW/scw5f6TRwoN3h2L3bfuaHr9ANDRliYf1ZZ9krlEaPTjxuKyy02Xv33GNP1sbtMQ9iJ8rA/XVJE51zh0n6QNI8SR5Wo5GycMNUHwN3yea4E7hnXkEBDfdcsX27pSkf+UjUK+lYfr70ne+k9z61tdae27LFju3bkzfp6ura3gGeNEk6++zOvy4TJ9r1JOm99yzBWr7cbqOuru2xZ0/izzU11goJ56y3p73Avrzcbs85+1n4L/8inXRSovZaU2PnggLp4x9P72sXNQJ3oAXmx0NSInAvLo52Hb3BucQr2UpLM3e7w4fb78Q4/V4cPNj+zQncu6e+3u43xWHTVCkxUiYI/BvtGkf/8i/2KpdU5efbL8xwXEpYqGke9BYWWsAcPoYI73uXlVm4vG1b4vaGDrVfruH7VVdL//M/0vXX+/84rLl16+x8xBFt3+ZcYpRMqLjYGu0XXJC47MCBtq8ubq662p4cray0r317T6A2d9dd1nD/5CfT+3yANEUWuAdB0OCcu0bSM5LyJN0dBMHqqNaDDAgD90mTol1He6ZNs2Zp+OjQN3EN3Gm4546yMjsfdli06+gJhYV2B7a37sQefrh09dXpvU8Q2IO/8EmBLVustZ8srG99DBkinX663bGMS1srVQTuQJcwPz7LxbXhju6bMIHAvbvC75+43GcaOdJ+sO7fzwbymbB6tfSZz0g//KHthxEeycag1Nfbkx3bttmxdasF6NXViWC9tfx8GzPZr5811886y8anTJ1qM8xb/9z+619tH6i1a+MVuK9fb+fujCPtbJZ7//52HHpox9cLnXiinV97jcAdPS7KhruCIHhK0lNRrgEZtHKl/aDztUkzbZo1Vt98Uzr55KhX01YcN02VaLjnkg0b7JyNgXscOGfB+ZAhtpkQDIE7kDHMj88iBO65q6SEwL27KivtHJeG+6hRdt6xg8C9uw4csMc8X/iChd/dFQQWytfUWNAejoFMR7iXwJo1iVfLxsH69fYEoE9lx2HD7AmApUujXglyQIwGQMF7K1f6226XbIyC5O9Ymfp6mwEXp7lsEg33XBI23DP5cm2guwjcgV63aJGF71Om2DkM6sP58UuX2nnbtraX7d3bNpi/5BLb7zp8TN6/v4X6c+a0vKxPn0Qw39RkZ+esKCi1bMOHkl22dat06qktX8GfdXJppAxaInDvvnB/nrgE7iNH2nnHjmjXkQ3efdd+wRx5ZGZuzzkrqA0ZYmNm0g3bJXvidOxYC9zjZP365ONkojZjht0pCbJoi8ggsNn/Tz0l/fjH0s9+Jj33nN3hyabPM2Yibbgji9TX20ucZs+OeiXtGz/e7owsXx71SpKrq4vfOBmJhnsuKSuzeqBPLQWAwB3wBvPjPVJVZU3XuL1yEt1XUmIbCu7ebSEf0hc23OM0UkYicM+EcO54pgL3TDnqqHgF7kFggfs//mPUK2lrxgzpv//bAupwo+k42LLFXn0Rji8KRxitX2+bJu/dm/z9hg2zYmxpqTUUmu8rMGyYNGuWNSjy8tq+7/790ssv21ijQw6xsUfHHmtPHqFTBO7IjLffttDd1w1TJXskNm2avw33uAbuNNxzR1kZ42Tgn7DaSuAOxEac58dLMQniq6oYJ5OrJkyw86ZN0uTJ0a4lruI2UiYM3MvLo11HNuhoo88oHX20dN998dkYd+tWm3nv29dRkk46yc5Ll8YncH/7bXvSpakpcVl+vo2TmjhR+tKXLFSfNMlGj9bX214Eq1bZsXKl9OKLNjIpvDN04EDitoYMkU45RTrtNLuNv/1Nev55C9pbZz3O2VieqVNtg9vPf743vgKxROCOzAg3TPU5cJcscH/uOZuhFoY0vohr4F5QQOCeKzZssA17AJ/QcAeyQlzmx9fUpL6Ra6TB/K5dBO65KgyQNm8mcO+qcKQMDffcs26dPWnlW4P36KOtwfzBB/bKfd+FG6b6GLgfe6xlQUuXShdeGPVqUvPIIxa2P/64dPjhdqdi2LCOxxGPGtXxxrBNTdLf/y699JKF8S++KD35pL3NOem446RvflM64wzLAHbssA2Bw+Nvf7NXghC4t4vAHZmxapW9BMW3l161dvzxUmOj9NZbiWc2fRHXwL1vX0bK5IL6eun996WPfCTqlQAtEbgDOaW9YD6cFZ9uQz5ZCJ+sHd/R6JrCwkCXnluhJS8P1/x/2a3bf1ojDRqkm+YP0JIlrtNgvkfQcM9dYeDOHPeuq6y0x7aDB0e9ktQMHGibcRK4d9+6dX5mGkcfbec1awjcu6tvX2n69HhtnPrYY5ZfffazmbvNPn3s/9JFF9kh2RM6a9ZY2D5iRMvrl5TY0XwNzRv3aIPAHZmxcqX9MO3XL+qVdGzaNDsvX+5n4B7HOZs03HPD++/bL1RGysA3YeBeUxPtOgD0jCCwMQkFBZZ8FxQkfTl9T8+PTzq6pl+jauucamv7aMEj9sB0wQPFWvBAy41KPwzm3QFdesgzWvLBOZp/6mLd/unHpEGDtFVjNe/huXro4ic0pv8eqaFBW6sKNe/RhUPToQAAIABJREFUz+uhC36vMUMP2EvH8/Lsc9+1S9q5M3FUVNgD5+ZzWVeskE4/PWP/DIiRUaPs+4TAvesqK+0JqziM7pBsnaNGEbh3VxBY4P6Vr0S9kraOOsrOa9ZIZ54Z7VpSsX69tch9fXLgpJOkW2+1sSq+Z1ibNtlY5B/9qOc/1rhxdqSqo4Y9CNyRIStXSieeGPUqOjdhgr000Mc57vX1NNzhrw0b7EzgDt/QcAey289+Jl13XeLvffpY8J7sGDDARiuMGZM4Ro9usdn3ou8c/ENTP932rWKpuFhzvzJEV17ZR1dcHmjhrQe0dV2NVF2tK2fs0BUTF2vhihO19bnBGjvgvzV49RzVVs9QoepUe6CvLnG/VcO4CXp8x0xV1+Wrf0GDZh+1SUFjo55ZV6rqhgL1zzug2sZ81Qb9tOCD8yRJC97+lBa8/SkVqkaX6h4t0VjNv0m6Xd+VJN2k27REEzT/p/11u7794fq3aozm6UE9NOxqjRkdWAMtfPVZOB9n1y77fZ3JJhzio08fe8yzeXPUK4mvior4jJMJjRxJ4N5dH3xgc8d9bLiPHGn/J+Oycer69Tbn29dAdsYM6Sc/kd580/8c6/HH7TxnTrTrQNoI3NF9e/da5cfHZ4Jbc87GyvgYuMd1pExBgd0xQXYrK7MzI2Xgm7D1SeAOZJ+qKhuYPnOmdP75Vitv76ipsbBpzRpp27aWm4F1YpFkFfbfBLpt796Wb3yjn27Ly7M/3y5tr52lKwvu1hWfWKuFfa7U1sJ5Gj2ur2oXHmzD1+Vr9McPt4b8mvCyfrrkS+3Mj28q0gJdLUlaoKs//HMovKywMFBN+T7d9I0CLbm3QPMvXJ3a/PjZHm/sip5TUkLDvTsqK+OzYWqIwL37wg1TfQzcnbOxMnEK3KdPj3oV7Zsxw85Ll/ofuC9aZBuZTpwY9UqQJgJ3dN/q1Xb2fcPU0LRp9mymby8fimvgTsM9N5SVWbDp68sCkdvCmQ8AssuPfiTt3m0D0NPZ/DEI7P22bbMj2c+HsAne/AgCqbS05VFc3GKsRLPJNQon1yQbUyNlen68kxs86MOP3Xwj10svbbuJa6obuyJLlZRIf/pT1KuIr4qK9MYq+GDkyERgjK4Jv37h+BbfHH209PDD9kvC53FHtbXSxo32S85X48bZ49rXXot6JR0rL7df5jfcEPVK0AUE7ui+lSvtHKfAvaHB1u3Ts65xDdyZ4Z4bysrs5clhyw/wCYF7ZjQ2SnfcYWMICgrs6Ns38efWx4ABNs4iPAYO9PsBIOLlgw+kX/zCNvJKJ2yX7P9hsY2L6Y2mYiTz4/snJsgsWGC3H4bwzXUWzHfYjieYj68JE+wf0reCUVxUVsbnsW2Ihnv3rVsnDRliP4h9dPTR9sqv8nJ/1yhJ775re3/5uGFqcyed5P/GqU88YV/LuXOjXgm6gMAd3bdypT3oLi2NeiWpCTdOfeMNAvdMoOGeGzZsYJwM/EXg3n07dliw+fzz9vWsr7cAPh19+9omc3372itiwqNfP0vtDj3U2kTjx0tjx1qjeMuWlkcQWHDf/DjnHOlzn+uZzxv+mj/f/g/Onx/1SjImWTCfajt+7FgL3rvSkG8vmE+nHS8RxMdKSYmdt2yRDj882rXEUVxHylRXS1/6km1WGW6gXFRkG6q23ttiwIDufbymJvs6bdsmbd9urwoIgpbXKSqSzj47PoWddevsSVpfywPNN071OXBfv97OvgfuM2ZIv/+9PYExalTUq0nusccsZ5syJeqVoAsI3NF9K1faTClfN8RorbTUAgHf5rjX1yc2/4sTGu65oayMwAv+InDvntdflz7/eXvAceed0mWX2eWNjfbz/cABe2K19bFvnz3A3rkzcezaZQlgfb2dGxrs32brVru/sG1b2wfkxcUWwo8bZwH9vn12/X377DbvuUf64x+lf/iH3v/aIBpvvy3ddZd01VVZ/2Rvqu14yTKtrjTkkwXz6bbja2pSH1NDMO+BMHDftInAPV11dbZHWdw2TT39dHtM/vLLiW/w8EimX7/EK4GKi63ZnZ+f+N3d0bF/v/1AamjofF0PPihdeGFmP9eesnat9OlPR72K9h19tJ3XrJE++clo19KRMHD/2MeiXUdnwjnur70mnXtutGtJZs8eK8Jcc42/TwKhQwTu6J4gsAfQcdoxOdw4dfnyqFfSUl2dVZfihoZ79tu3z9qvhx0W9UqA5HwP3IPAguyf/MRaXgMHWrMsPAdBag9wWz84HjrUGnjNW2tjxtjtVla2DML375c++lHpmGPs/Zqv65prrD77yiuJV4FJtta8vMw+GVxfb2nc3/9u6xg3ztbbnn37pFmz7MH6X/9qYQKy34032v+7G2+MeiVe6U5Dvnvz41s+1md+fAw0D9yRnqoqO8et4X7SSYlRr801Ntr9+HBPi/Coqmq5h0VVlbXWm79Craio5d/z8+2xX1FR2/sew4e3LOAFgXTqqfaEeRwC9z177L6Jjxumhg45xPKCtWujXknH1q+3tQ4a1Pl1o3T88XY/19fA/amnLGeJU9aGFgjc0T3btlm7LW4z7qZNk37+c7/GuPi0lnTQcM9+ZWV2zvKWIWLM58C9okK6/HJ7SeiMGTZWZd8+Oz74wM59+rR9QBs+qO3f3/6cl2dN8x07pHfeSTxATqVd1tq4cRZc9+1rD4TPPFP63e96p83Xt6/NFp4wIbXrDxwoPfmkdOKJ9mDotdf8fdkvMmPZMumRR6T/+3/9fsm8J5gfj6TGj7dnSQjc01dZaee4Be7tyctLhOK9bfZs27y3sdH/sTJhK9vnwN05a7mvWRP1Sjq2fr3/42Qk+2U2ZYq/c9wXLbJfzCefHPVK0EUE7uieVavsHLfA/fjjLeBevNh+yDbfBK5v32heshPXwJ2Ge/YLA3ca7vCVr4H74sXSF79oo1p+8hPpm9/M7Pi1ILBG1vbtLVtre/ZYeN58Q9OiIhvTsWqVHatXSxs3/v/27j3Kyrre4/jnywzMcJcAuYkXki5iqQVq6lHKjpC0RNAKY1lnZYqadTwGppnnuKzWIjW1VWpq2TGvq1V4umCStFSqE4EaeQMKwQtHIGACQZwLw+/88d1Pe8+evWf2zOzZz/Pseb/Wetaeefaeme/smf3bv+f7fJ/vzyuIr7su2QfCEyf6olGnnupVPr/9bTpbsKE0V1/t/78LF8YdSVWhf3wfM2CA/+Feey3uSNJn506/TVtLmSSaNUu6/35p1arkJw3XrfPbJCfcJU+4L10adxTFheAJ93nz4o6kNCeeKN13X/JOCjU2eoX7/PnJigtdQsI9bR54wGew0SIopW61tb2TRI4uW0vbJd7TpvntzJmF7+/f37coCT90aNvExahRfjl/7oI0dXXeGmDMmGwVwaBBbb9vVDa0b58fufTvn70vrQl3KtyrHwl3JF19vV8GvHx5+wU3hwzx8bnc74EtLZ5I37rV27XkVqbX1HhWaPFiafJkr5z5wAfK+/Ml/52GD/etlD6ZRx4pnXlm+eOohGnTpHvv9cvSL7xQ+vGP6WfZUyFIixZJTz7p85WBA33rysf19X6Qmt/+qKam/RUbTU3Zqzuibf/+to/ZudNfxzffnM42eylD//gqd9hhVLh3R7VVuMdpxgx/P1i6NB0J99ra5K958N73Svfc4++XSTwptH27X4GZhgp3yRPut9/uf/8pU+KOJuvxx/34Yu7cuCNBD5BwT5v//E9p48auf12/fqUn5wcO9OTEvn3S7t1te7u9/Xb2YKqlxQ+yxo71VdHTZNIk6bHH/HL+QgvB5W7RAeKOHdlF33bs8OenM8OGeXI+9yCztTV7/6hR2eT8G2+kM+FOhXv127TJTyaNGhV3JEBhY8d61XOxha6ivunFtsGD21ePhOBjW/7CYw0NnmTfsaPzuC64wNuXddSjHKX75Ce9Sv/aa32ucswx2RPk5djq6toXKIQgbd7sB2LRtmuXzyOOPNK3yZN9HpS2EwC33CJ9+9veI7+21ud8W7f6XG/fPr+NtkqaNMkXS0Wi0D8+hQ47zE+o3X+/n5iN1v8YMqT9+1tTU7bYqK4ue1vo47iuBq4UKtzLZ8QI6aSTvFL3G9+IO5qOrVvn7+m5BXFJFC2cunatv38nTdSaJy0J9xNO8NuVK5OVcH/kER+3k7w4LjpFwj1tVq3yA5/8BEA5tt27234+aFB2YnbIIf6Cj5ISudVIJ50U97PSPTNm9OzrW1raP4d79mQrHqNtxw4/KshN7gwa5AfsuY8bPz6Zb5qdocK9+m3c6AmQaj64Qrr94AfSFVd4JUh+BW1H25Yt2Y9DaP99Bwxoe0K6rs4Pxk45pf0ipflVvmPHpvf9McmuucZPAt59d+98/9wChYED/b36rbey9w8b5gmEhx/2xeUiY8Z4dvGss3onrnL73e+kK6+UzjnH+6V3NL5HJcu5ifjotrGxfSV7TY0/N7mvh5aW9nOhwYP98fmvnaFD/bWGxKN/fMJNm+Zj1fnnl/975ybiBwzw133+lS4htC/sGj/e37MnTix/TOVChXt5zZolXXWVF5eNHx93NMWtXZv8djISCfdymzzZ53UrV3qhTBLs3++tFD/+8XQWZOKfSLinDWfakyOqiEv66tu9rX9/P7BOWt8zlM+mTSyYimSrr++dli1IHjPphz/0nvjNzZ7ILbRFSd6ubE1Nbas9Gxs9Kfye92S3MWM8huZm74G/YYP08svSj34kzZ4tfeUrXsVXm+Ap9tat3ppn0iS/LL2zk6lm2VYyvZGASno1IXqM/vExueIK6TOfaXu18q5dfpK5rq5tInzAAH9Sm5qyV/iW8nF029rqr+Xck28hZB8f/TGWL5c+9jE/6TdiRNzPUGENDR5/Xz/GK5czz/SE+6OPSp//fNzRFNbS4u/ns2fHHUnnDj3UB7ikLpy6fr2PL4cdFnckpTHztjLLlkk33NC+80NUgBptw4d3nASPxr29ez1HkluMUFvr/2v5XSS2b/fOC5s3+7Zpk19pM2dO5Z4H9IoEHw0ASIXoDaelhYR7NQrB3/RPPz3uSAAgK+5EzYAB3jc/6p1/4YXS5ZdL3/qW9Mc/elXpuHHxxljI/v2eOdy1yw8u6ZOOmNA/vkKitaeS4okn/OzI2Wf7GJTEBbB37vSTi1zZWR5HH+1XNCxdmtyE+6ZNfiybhgr3fv28j3uSE+6TJ6crLzB7tvTrX3vRRClqatpfAVushXBXjBzpnSUOP1w64wyvcEeqkXAH0DNRZVpzczInzeiZHTu8nQIV7gBQXH299P3vSyef7JnB447zpPv06XFH1tbXviY99ZQvQPu+98UdDVAS+sdXkQ9/2Mef887z6vuHH/YEYpI0NNBOppzMvK3Mffd5UjKJLcPWrfPbNCTcJU+4P/lk3FEUtn59+uYXCxb4yaDcq3EaGwuvabhrV9vHRO31orZ5gwe3bZsXtdiK2ufV1HjRSG7F/DveIU2Y4BX1qCok3AH0TG6FO6pPtEjzEUfEGwcApMH553uy/dxzPbE0frxXK0XbhAnZg7BybSEUXj/moIM8wzdmjN9u2OAV+AsWeKILSLFq6x/f0f6qM2+et09YuNDHxFtuiTuithoaaONabrNm+UnpFSuKL3Afp7Ql3I86yhdDfvPNZF2p1tzsbfbOPTfuSLqupsYH+UGD4o4EVYSEO4Ceya1wR/XZtMlvSbgDQGmOPlpavVr63vc8yb15sx/ML1/uB8eVMHy4L+Seu7CrJH3wg9Ktt1YmBiAB0tI/XupjFfJXXCG9/rqPRxMn+udJsXNnshd1TaOPfMQr2x99NLkJ93Hj/L0zDaKFU9etk44/Pt5Ycm3c6JXcaVkwFehlJNwB9AwV7tWNhDsAdN3QodLVV7ffv3evZ+SiS4xzLzPu7iZJBx/s2bixY6XRo/29ubXVK92jqveGBmnGDNq/oc9Lcv/43P1dqZBPXWLeTLr5Zq90//KX/WzDkCFttwED/Cqe3MVYo7YN+Y/LHxcPHGjbY7m+3r/+zTezbSF27/YTkwcf7D2To237dumYY2J+gqrMoEF+1dfSpcm7okHyxHVaqtulbML9pZeSlXBfv95vSbgDkki4A+ipqML9xht9UaZoFe7crdC+Yvs72tfa6pPj3F5qe/f6JC534j10qPeOGz8+3uemGmzc6AcigwfHHQkApF/0PlUJNTWeCRwzhuQR0E293T9+zhxp0SLphhtKS85XVf/4fv28r/fUqZ54jxYcjLbdu9sm0Vta/EmJ7u/J1bU1Nd52a/BgP6vS1NT2/tGje/a7ob1Zs6QvflH62998Uc2kCEFau9bXFUiLI47wKwaStnAqCXegDQshxB1DyaZOnRqefvrpuMMAkGvVKun0033imzQTJ0onnujbCSd44jhfTY1XxuRvNTVtV8rqrhB80dG6uuzJiTT56Ef9b7tyZdyRAAAApMbcud6lIj8Jf9ddPtVsbvYlFW6/Xbrkkrb7i1XI53eJKiZKzN95Z/ZnSFXWP7652efYzc1tq+Bra30On78AYkuL9wgaPtwT7dE8/8ABXwfjlVekV1/1NmDnnOPV7iifTZukSZO8wv3yy+OOJmvbNv+n/853pC99Ke5oSvf+90uHHir96ldxR5J1wQUez7ZtcUcCVJSZPRNCmNpuPwl3AGVz4IBXoedu0eXyxT7vyj6z7Ire0aregwb5hDq3IqahQXr2WU8Sr1zpk+euMmubgI8m7/mPqatre7lqXZ3HkLuSeWurP/6gg/wqgGg77TSf2EVteZLone/0SxUfeijuSAAAAFKtUBJ+yZLSkvNd6R9fKDFfLAkvSZdeWnpyHui2o47yhXIffzzuSLKeekqaPt1fRGecEXc0pZs3zwvfNm6MO5KsU07xK1dWrIg7EqCiSLgD6Lu2bPEF7PbsaX9fa6sfxXS2FepRf+CA35dbPdPY6O0CopMCI0Z4NU1Tk/fS3bHDe0Nu2SK98II0ZYof4Zx8cu8/D121f780cKB05ZXSN78ZdzQAAAB9RqkV8iGUtzpe6lqFPIl5lGzRIq8kb2ioXHu1ztx5p/eDevVVrxhPi+uvl667zo9v42792dzsBWfvfrd09tnS3XfHGw9QYcUS7vRwB1D9xo2Tzjor7ija++Uvpcsu82qAiy6SFi/2BH1SbN7sR2wsmAoAAFBR9I9H1TnzTOmmm/yfY8KEwm09czepfWFTTY1fgXvkkb6NG+dV1d21bp3/4x9ySHl+x0qZMsXPtg0Z4i/OQYM63szar4lQaCH2aH2D3K2+3k+SRMVjO3ZIO3d6sn/v3raFaVOmxPecAAlDhTsAxGnvXq9OuPVWaeRI72mYX/Fh1r5tTV2dlyrlT5JqazufvA4Y4F+bvzhVc3Pbn7N2rR/BLV/uffoBAACQePSPRyK1tEgzZkgvv1z4iuLO1Ndnj3kiAwd6v/2RI73daJQkHjbM788/Vurfv+3xzoMP+j//M8/0yq/caxobvZK8ocFfqB1tb73lX5O7zkH+ugfRtn9/29ao//iH/93yW6OOHOnPcbQY/JAh/vyfc05yrl4AKoSWMgCQZH/+sx+VrF4ddyTtvfaaL0ALAACAVKJ/PBItBP8ny03Ah+AJ9fp6/2eMqrRff13asCG7vfJK2wTxrl3S7t1e+Z6fUG5p8X/Upqbsz77sMum7343tV0+0EPxFXVMTdyRAYpFwB4CkC8GrFPIdOOCTwtzLKZuafOKTO4GsqSm9J72ZNHRo26qE/v3b/5zhw5PZXx4AAAC9gv7xqHohZNfiGjbMj40AoBtIuAMAAAAAgC4rlISXulcd31H/+K5UyFMdDwCIGwl3AAAAAADQa+gfDwDoS4ol3HuwnDMAAAAAAIBbskS67TbpmGP8dskSads26eKLpZUr/XbrVn9s/v49e7y7R2OjJ88bGz0J/+lPe/Jd8tu5c71KPndfv37++Dv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      "text/plain": [
       "<Figure size 1872x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_acc = []\n",
    "real_high_prices = []\n",
    "price = []\n",
    "predicts = []\n",
    "predict_count = 0\n",
    "\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    # Restore\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('checkpoints-cnn-max'))\n",
    "\n",
    "    for x_t, y_t, z_t in get_batches_exp(X_test, y_test, X_test_prices, 1):\n",
    "        #print(y_t)\n",
    "        feed = {inputs_: x_t, labels_: y_t, keep_prob_: 1.0, is_training_: False}\n",
    "        batch_acc, logits_  = sess.run([accuracy, logits], feed_dict=feed)\n",
    "        #print(logits_[0,:])\n",
    "        sort_list = sorted(range(len(logits_[0,:])), key=lambda k: logits_[0,:][k])\n",
    "        predict = sort_list[1]\n",
    "        if predict == 0:\n",
    "            predict_count += 1\n",
    "        elif predict == 1:\n",
    "            predict_count -= 1             \n",
    "        predicts.append(predict_count)\n",
    "        real_high_prices.append(z_t[0])\n",
    "        test_acc.append(batch_acc)\n",
    "    print(\"Test accuracy: {:.6f}\".format(np.mean(test_acc)))      \n",
    "    \n",
    "    X_pred,_,_,_,_ = read_stock_data_items_and_transform_by_stock_num(data_path=\"./data_sets/predict/\", stock_code=\"sh.600000.浦发银行.csv\", n_dim_feautures=8, hold_days=10, predict_days=5)\n",
    "    \n",
    "    feed = {inputs_: X_pred, keep_prob_: 1, is_training_: False}\n",
    "    predict_ = sess.run(logits, feed_dict=feed)\n",
    "    print(\"Tommorow stock : \")  \n",
    "\n",
    "    range_ = np.arange(len(predicts))\n",
    "    # Plot Accuracies\n",
    "    plt.figure(figsize = (26,6))\n",
    "    mean_ = np.mean(real_high_prices, axis=0)\n",
    "    print(mean_)\n",
    "    std_ = np.std(real_high_prices, axis=0)\n",
    "    real_high_prices = (real_high_prices - mean_) / std_\n",
    "    predicts = (predicts - np.mean(predicts, axis=0)) / np.std(predicts, axis=0)\n",
    "    plt.plot(range_, real_high_prices, 'r-', range_, predicts, 'b*')\n",
    "    plt.xlabel(\"iteration\")\n",
    "    plt.ylabel(\"price\")\n",
    "    plt.legend(['real', 'predict'], loc='upper right')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
